Bayesian density estimation and model selection using nonparametric hierarchical mixtures
Bayesian density estimation and model selection using nonparametric hierarchical mixtures
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DOI：
10.1016/j.csda.2009.11.002
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发表时间：
2010-04-01
影响因子：
1.8
通讯作者：
Pievatolo, Antonio
中科院分区：
文献类型：
--
作者：
Argiento, Raffaele;Guglielmi, Alessandra;Pievatolo, Antonio
研究了一类非参数分层混合模型的贝叶斯密度估计问题。这一类，即混合的参数密度的正实数与一个规范化的广义伽玛过程作为混合措施，是非常灵活的检测数据中的集群。与几乎肯定的近似的混合过程的后验轨迹的马尔可夫链蒙特卡罗算法来估计线性和非线性泛函的预测分布。通过最小化参数与非参数备选方案的贝叶斯因子来找到最佳拟合混合度量。模拟和历史数据说明了该方法，找到最佳拟合模型和正确识别混合物中组分数量之间的权衡。(C)2009 Elsevier B.V.保留所有权利。
A class of nonparametric hierarchical mixtures is considered for Bayesian density estimation. This class, namely mixtures of parametric densities on the positive reals with a normalized generalized gamma process as mixing measure, is very flexible in the detection of clusters in the data. With an almost sure approximation of the posterior trajectories of the mixing process a Markov chain Monte Carlo algorithm is run to estimate linear and nonlinear functionals of the predictive distributions. The best-fitting mixing measure is found by minimizing a Bayes factor for parametric against nonparametric alternatives. Simulated and historical data illustrate the method, finding a trade-off between the best-fitting model and the correct identification of the number of components in the mixture. (C) 2009 Elsevier B.V. All rights reserved.